后疫情时代高校教育教学模式重构与探索

新型冠状病毒肺炎疫情对我国高校信息化教学基础与能力进行了一次全面检验。随着疫情的缓解,教育教学改革的迫切性进一步凸显。在后疫情时代,教育教学不是重新回归线下,学校应将学生学习渠道由单一的课堂教学活动向信息化、立体化拓展；学生学业考核应从知识的获取与复现评价为主向学习成果产出转变；教学质量保障体系应从线下的刚性督导体系向以信息化为基础,以目标达成度为核心的线上线下结合方式转变；专业办学应建立服务于“四新”经济的开放灵活教育教学体系。     

电路课程混合式教学研究与实践

本文介绍了我校依托精品在线开放课程资源开展混合式教学的研究与实践,并以我校电路课程为例,阐述了在该课程中通过实施基于雨课堂和雷实验的混合式教学促进学生主动学习的具体实施方案。学生的考试成绩以及教学评估结果表明,这种线上与线下混合、理论与实践混合的混合式教学方式极大地调动了学生的学习积极性和主动性,提高了其主动学习的意识和能力,收到了很好的效果。     

基于网络的俄语移动学习模式的构想

在如今这个互联网时代、移动时代、云时代等多元时代,在慕课、翻转课堂等教学改革的推动下,文章以现代网络技术云计算理论为依据,对俄语移动学习模式进行了初步的构想,着重提出加强俄语教师开发移动学习模式中俄语微型课件的开发的重要性,以期能够提高学生学习的兴趣和改进俄语教学质量。     

浸入式可见/近红外光谱技术的藻种鉴别研究

对藻类的识别分类及其生化分析已成为海洋生物学的研究热点之一。以普通小球藻、蛋白核小球藻、微绿球藻、莱茵衣藻为样品,通过便携式USB4000微型光纤光谱仪、Y形光纤和探针,卤素光源构建的光谱采集系统对不同浓度梯度的120个微藻样本进行浸入式可见/近红外透射光谱的原位采集,比较去基线、卷积平滑等光谱预处理方法的效果,并基于连续投影算法(SPA)筛选特征波长,通过偏最小二乘法(PLS)、最小二乘支持向量机(LS-SVM)和极限学习机(ELM)进行建模,探讨采用透射光谱原位快速鉴别四种不同藻种的可行性。结果表明：卷积平滑的处理效果较为理想,有效波长可用于代替原始光谱建立微藻种类判别分析模型。SPA-LV-SVM和SPA-ELM的预测效果显著高于SPA-PLS,三者的平均预测正确率分别是80%,85%,65%。浸入式可见/近红外光谱技术和便携式光纤探针结合的藻种鉴别方法,有效实现了对四种微藻的鉴别,为藻种鉴别和藻种分类研究领域提供了一种新思路。     

Learning effects on maintenance outsourcing

The objective of this paper is to study learning effects on maintenance outsourcing. We consider a situation in which a manufacturer offers a short-term outsourcing contract to an external contractor who is responsible for scheduling and performing preventive maintenance and carrying out minimal repairs when the process fails. The manufacturer’s payment to the contractor consists of a fixed amount along with cost subsidization for each maintenance operation performed. We assume learning occurs when the contractor performs preventive maintenance that reduces both time and cost. Two types of learning are considered: natural learning and learning by costly efforts. We demonstrate that a well-designed payment scheme can induce the contractor to adopt the maintenance schedule that maximizes the manufacturer’s profit.     

Learning about preferences in electronic negotiations – A volume-based measurement method

To create an integrative solution in a bargaining problem, negotiators need to have information about each other’s preferences. Empirical negotiation research therefore requires methods to measure the extent to which information about preferences is available during a negotiation. We propose such a method based on Starr’s domain criterion, which was originally developed for sensitivity analysis in decision making. Our method provides indices for the amount of preference information that can be inferred both in negotiations reaching an agreement and negotiations where an agreement was not (yet) reached. To test the external validity of our proposed measures, we conduct an empirical study which shows that the proposed measures exhibit positive relationships to the success of negotiations as well as to the efficiency of outcomes that would be expected according to negotiation theory.     

Learning study – promoting and hindering factors in mathematics teaching

This article focuses on presenting success factors for a group of teachers in carrying out a learning study in mathematics at their school. The research questions are: what are the actions of the school teaching community during development projects? What factors enable a group of teachers to carry out a learning study at their school? Activity theory provides a holistic framework to investigate relationships among the components present in a learning study. The results are based on analysis of interviews with teachers, students, principal organizers of schools and project coordinators, videotaped lessons, students’ tests and minutes taken at meetings of mathematics projects. The results show that the skills of facilitators, the time devoted to collaborative work, the link to learning theory and avoiding overly comprehensive content when teaching lessons are important promoting factors in mathematics teaching. The findings raise important questions about the way in which teacher work within universities.     

Towards an integrated theory of mathematics conceptual learning and instructional design: The Learning Through Activity theoretical framework

We discuss the theoretical framework of the Learning Through Activity research program. The framework includes an elaboration of the construct of mathematical concept, an elaboration of Piaget’s reflective abstraction for the purpose of mathematics pedagogy, further development of a distinction between two stages of conceptual learning, and a typology of different reverse concepts. The framework also involves instructional design principles built on those constructs, including steps for the design of task sequences, development of guided reinvention, and ways of promoting reversibility of concepts. This article represents both a synthesis of prior work and additions to it.     

Simon’s team’s contributions to scientific progress in mathematics education: A commentary on the Learning Through Activity (LTA) research program

I discuss two ways in which the Learning Through Activity (LTA) research program contributes to scientific progress in mathematics education: (a) providing general and content-specific constructs to explain conceptual learning and instructional design that corroborate and/or elaborate on previous work and (b) raising new questions/issues. The general constructs include using instructional design as testable models of learning and using theoretical constructs to guide real-time, instructional adaptations. In this sense, the general constructs promote understanding of linkages between conceptual learning and instruction in mathematics. The concept-specific constructs consist of empirically-grounded, hypothetical learning trajectories (HLTs) for fractional and multiplicative reasoning. Each HLT consists of specific, intended conceptual changes and tasks that can bring them forth. Questions raised for me by the LTA work involve inconsistencies between the stance on learning and reported teaching-learning interactions that effectively led to students’ abstraction of the intended mathematical concepts.     

Denoising Convolutional Neural Network with Energy-Based Attention for Image Enhancement

In the realm of image denoising, the use of convolutional neural networks (CNNs) has lately gained traction. Several activities involve the utilization of excellent-clarity pictures and recordings. Images were captured in a wide variety of illumination circumstances, which means that not all of them are of the highest quality. Low-light photography suffers from a decline in perceived image quality because of the restricted dynamic range of the pixel values. Therefore, it is vital to enhance the appearance of images. Maximum texture retention is achieved by the structural similarity index-loss-based method. The suggested discrete wavelet transform (DWT)-self attention (SA)-Denoising convolutional neural networks (DnCNNs) make use of state-of-the-art techniques for image denoising like energy band analysis, very deep architecture, learning algorithms, dense-sparse-dense training, and regularization approaches. DnCNN is intended to remove the hidden layers" latent, yielding a pure picture. After a degraded input sample has had its relevant energy features retrieved using DWT, the perfect image enhancement is achieved thanks to the incorporation of the self-attention mechanism. Second, a hierarchical-branch network is formed by combining the suggested network with the denoising CNN and additional loss in order to reduce the reliance on the amount of noisy data in multi-modal picture analysis and make the problem of image enhancement more tractable. In the end, DWT-SA-DnCNN"s self-learning qualities are used to improve image quality by obtaining features including undesirable noisy data, edge factor, texture, uniform and non-uniform areas, smoothness, and object structure. Simulation results show that our hybrid DWT-SA-DnCNN-based contrast enhancement strategy outperforms state-of-the-art methods.